This dissertation addresses two classes of network flow problems in networks with multiple, stochastic and time-varying attributes. The first problem class is concerned with providing routing instructions with the ability to make updated decisions as information about travel conditions is revealed for individual travelers in a transportation network. Three exact algorithms are presented for identifying all or a subset of the adaptive Pareto-optimal solutions with respect to the expected value of each criterion from each node to a desired destin ....etc

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scada system design and construction for real time electrical parameter monitoring and control

Supervisory control and data acquisition (SCADA) systems have traditionally played a vital role by providing utilities with valuable knowledge and capabilities that are key to a primary business function - delivering power in a reliable and safe manner. A quality SCADA solution is central to effective operation of a utility's most critical and costly distribution, transmission, and generation assets.

The challenging issues for SCADA systems and proj ....etc

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lms algorithm adaptive beamforming matlab code

Abstract

Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce co channel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. This paper presents 8-elements array with inter-element spacing /2 an optimum value to avoid grating lobes. An Adaptive beamforming is a ....etc

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Abstract
Steganographic technique is a means of covert communi-
cation. In this paper, an adaptive image steganographywith
high capacity and good security is proposed. Based on lo-
cal complexity of a cover image, varying-depth embedding
is used to improve the imperceptibility and decrease dis-
tortions in it. Experimental results show that the stegano-
graphic technique may provide higher capacity and be re-
sistant to several well-known steganalytic methods.
1 Introduction
Classic methods of securing communicationmainl ....etc

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AIM AND KEY WORDS OF THIS TOPIC IS to detect the exact location of tumor without disturbing the entire image.

(E)EDGE PARAMETER(EDGE DETECTION)
(G)GRAY PARAMETER
(H)LOCAL CONTRAST, WATERSHED SEGMENTATION.

**BLOCK DIAGRAM IS AS SHOWN BELOW: **

MRI image as input --> preprocessing --> division into blocks -->
--> E --> G --> H --> watershed segmentation --> 2D visiualization

so far i tried like this..

firstly i have read an brain tumor mri image,by using 'imtool' command observed the pixels values. now as already we are ....etc

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Dear sir/mam,
i want to matlab code for my project phase.
....etc

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sir, iam final year student working on adaptive beam forming.can u please give us matlab code ....etc

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This article describes about modified band-pass filtered MFCC.Improved noise robustness is obtained by varying the time constants in each channel and adaptive gain compression of the envelope in each channel.This article also presents speech discrimination and
recognition results.
Mel-frequency cepstral coefficients (MFCCs),are not very robust for noise.Reasons include-the number of samples used to represent each frame is small compared to the original sampled waveform in most audio feature extraction processes.FFT followed by grouping into ....etc

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A Time-Varying Convergence Parameter for the LMS Algorithm in the Presence of White Gaussian Noise

ABSTRACT
A novel approach for the least-mean-square (LMS) estimation algorithm is proposed. Rather than using a fixed convergence parameter , this approach utilizes a time-varying LMS parameter _n. This technique leads to faster convergence and provides reduced mean-squared error compared to the conventional fixed parameter LMS algorithm. The algorithm has been tested for noise reduction and estimation in n ....etc

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Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal). It is a stochastic gradient descent method in that the filter is only adapted based on the error at the current time. It was invented in 1960 by Stanford University professor Bernard Widrow and his first Ph.D. student, Ted Hoff. ....etc

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